Local Differential Privacy for Sequential Decision Making in a Changing Environment. (arXiv:2301.00561v1 [cs.LG])

We study the problem of preserving privacy while still providing high utility
in sequential decision making scenarios in a changing environment. We consider
abruptly changing environment: the environment remains constant during periods
and it changes at unknown time instants. To formulate this problem, we propose
a variant of multi-armed bandits called non-stationary stochastic corrupt
bandits. We construct an algorithm called SW-KLUCB-CF and prove an upper bound
on its utility using the performance measure of regret. The proven regret upper
bound …

changing decision decision making differential privacy environment high local making privacy problem study utility